The role of sex and systemic inflammation in the development of cardiovascular disease in osteoarthritis: A population-based cohort study using the CLSA
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease (CVD) is increased in osteoarthritis (OA), particularly in females. This longitudinal, population-based study investigated CVD development in OA vs. no-OA. We hypothesized a) having OA confers an additional risk beyond the Framingham Risk Score (FRS), and b) systemic inflammation contributes to the OA-CVD link, and to a greater extent in females. METHODS: Data: Canadian Longitudinal Study on Aging cycles 1-3 (6 years of follow-up). Respondents completed questionnaires and provided blood samples. Individuals with OA were age-sex-matched to individuals without OA. Baseline FRS-risk (largely reflecting metabolic factors) and a systemic inflammation variable (comprising four factors) were derived. Cox regressions examined time-to-CVD for OA vs. non-OA, exploring the roles of FRS and systemic inflammation, adjusting for sociodemographics, comorbidities, physical activity, and BMI. RESULTS: Sample: 2123 individuals with, 2123 without OA. CVD incidence/10,000 person-years: 123.0 and 65.0 in females (p = 0.008), 187.6 and 166.4 in males (p = 0.670), with and without OA. Model results: Among females only, OA was associated with increased CVD risk (HR=1.78 (1.22, 2.58)). FRS-risk distribution was similar for OA and non-OA, and 'high-risk' FRS was similarly associated with CVD development for both sexes. In females only, OA was associated with higher systemic inflammation, and higher systemic inflammation with increased CVD risk (HR=1.77 (1.02, 3.05)). CONCLUSIONS: CVD risk in females with OA is underestimated by the FRS, an algorithm often used in clinical settings. While increased systemic inflammation, a potential intervention target, contributes to the OA-CVD link in females, there remains still unexplained increased CVD risk in OA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".